with a sentence about her occupation: "Angela Merkel is a German politician who is currently the Chancellor of Germany".

for relation extraction, this premise does not hold, and the model needs to reliably determine when a question is not answerable.


Only then the item was added to the test set for annotation.

apart from H, the conclusions we can draw about the other measures can only be preliminary, as there is no significance for their predicted ranks.


The accuracy is lowest when final embedding features are used for this task.

finally, we tackle the general issue of the representation of phonological form versus meaning with a controlled task of synonym discrimination.


Clark and Manning (2016a) build a similar resolver as in Wiseman et al.

in Figure 3(a) the baseline, but not L β=1,B 3 nor L β= √ 1.4,B 3 , mistakenly links 17 [it] with 13 [the virus].


It turns out that, B 3 and LEA behave quite similarly in non-extreme cases.

we therefore propose a simple solution that can turn a mention-ranking resolver into an entity-centric one.


Except from this change, the AE-SCL-SR model is identical to the AE-SCL model described above.

the number of pivots was chosen among {100, 200, .


As is standard in NN modeling, we introduce non-linearity to the model through a non-linear activation function denoted with σ (the sigmoid function in our models).

to these works our model can learn useful cross-domain representations for any type of input example and in our cross-domain sentiment classification experiments it learns document level embeddings.


We also propose a method of incorporating tag dictionary information into our neural models by combining word representations with representations of the sets of possible tags.

finally, we obtain the output label y t by performing a softmax over the tag set vocabulary.


To make use of this information, we propose an approach that jointly models multiple morphosyntactic prediction tasks using a multi-task learning scheme.

one reason for this is that in the tagging scheme for such languages, a complete PoS tag is formed by combining tags from multiple tag sets defined for each morphosyntactic category.


The bottom part of Figure 3 illustrates how to encode tag dictionary information for the word Hb ("love").

the independent model gives an accuracy of 87.74%, which is 1.53% absolute worse than CamelParser.


"Eyny" (my two eyes) and "qr$yn" (two piasters).

• When there is an elongation of a short vowel "a, u ,i" with a preposition, the elongated vowel is segmented with the preposition, e.g.


Table 3 further demonstrates the stable strength of our models since the results' pattern stays unchanged compared to those in Table 2.

the results are consistent with the Figure 3, in which the RALStM has better performance than the Enc-Dec and SCLStM on all domains in terms of the BLEU and the ERR scores, while the Enc-Dec has difficulties in reducing the ERR score.


(2016) that proposed a visual sentinel gate to make a decision on whether the model should attend to the image or to the sentinel gate, an additional gating cell is introduced on top of the traditional LSTM to gate another controlling vector s. Figure 6 shows how RAL-STM controls the DA vector s. First, starting from the 1-hot vector of the DA s 0 , at each time step t the proposed cell computes how much the LSTM outputh t affects the DA vector, which is computed as follows: where W ax , W ah are weight matrices to be learned.

the new input x t consists of information of the original input token w t , the DA representation d t , and the hidden context h t 1 .


Note that for the Cosine Similarity Graph, we compute the tf-idf cosine similarity for every pair of sentences using the bag-of-word model and add an edge for similarity above 0.2.

we compute the activation matrix in the (l + 1) th layer as H (l+1) , starting from H 0 = X.


Recently, it has become common to locally parametrize these models using rich features extracted by recurrent neural networks (such as LSTM), while enforcing consistent outputs through a simple linear-chain model, representing Markovian dependencies between successive labels.

he, she, her, etc., or the most general status of a nurse's shift.


For FOUN, "loss for all words" is the sum of "loss for new words" and "loss that are only related to old words"(which is a constant).

the operational costs are still O(m) ((iii) in Section 3.2.2).


The scientific corpus contains both common words (e.g., "propose", "experiment") and domain-specific words, such as "submodular" and "sublinear".

our method takes less than one minute to insert about 1,000 domain-specific words (e.g., machine learning related words from NIPS abstracts) into a pre-trained embedding model with more than 180,000 words.


7 (denoted by the black-dashed line in Figure 2) as: h e = g(W e h zu + b e ).

we performed the models in different scenarios as follows: (i) scratch training where models trained from scratch using 10% (scr10), 30% (scr30), and 100% (scr100) amount of in-domain data; and (ii) domain adaptation training where models pre-trained from scratch using all source domain data, then fine-tuned on the target domain using only 10% amount of the target data.


The original conditional probability p(y|d) modeled by a vanilla encoder-decoder network is thus reformulated as follows: This latent variable enables us to model the underlying semantic space as a global signal for generation.

the original conditional probability p(y|d) modeled by a vanilla encoder-decoder network is thus reformulated as follows: this latent variable enables us to model the underlying semantic space as a global signal for generation.


the satellite dinlas 18 has a 4 hour battery life .

such models have proved to work well only when providing a sufficient in-domain data since a modest dataset may harm the models' performance.


The substantial amount of missing past click logs on the newly-formed relationships also raises the necessity of an dynamic measuring approach.

the output of the attention layer is then passed to a fully-connected layer with non-linear activation to obtain the temporal representation.


In this setting, the goal is not to quantify the semantic relatedness between two entities based on their occurrences in the data, but to optimize the partial order of the related entities in the top positions.

the TS-CNN-Att performs better than the no-attention model in most cases, improves 11% for Pearson@10, and 3% when considering the total rank.


In the following, we provide the first analysis of the clickstream data to gain insights into the temporal dynamics of the entity collective attention in Wikipedia.

prior work on semantic relatedness often neglects the time dimension and consider entities and their relationships as static.


The procedure is repeated k = 100 times to eliminate the effect of data order, and the results per slice are averaged.

the hierarchy-related effects are reported in some studies.


(4) may not be sufficient for generating meaningful adversarial examples: they can lead neural NLI models to violate available background knowledge, but they may not be well-formed and meaningful.

if we just use the final decision made by the neural NLI model, we can simply check whether the rule is violated by two given sentences, without any information on the degree of such a violation.


Taking into consideration both the neighborhood effect and flexible letter encoding, we define the following research question: are the neighborhoods also defined using flexible letter encoding?

this, in turn implies that the neighborhood effect is likely a late effect, and is caused by concrete representations co-activating similar representations.


Compared with these studies, our work incorporates both document-level and corpus-level con-textual information with attention mechanisms, which is a more advanced and efficient way to capture meaningful additional features.

considering the limited contexts within each individual sequence, we design two attention mechanisms to further incorporate topically related contextual information on both the document-level and corpus-level.


The pooled features are then transformed to predictions over the output vocabulary V, by linearly mapping them with a matrix E ∈ R |V|×f L to the vocabulary dimension |V|, and then applying a soft-max.

this apparently small difference in connectivity has two important consequences.


To the best of our knowledge, the results of Wang and Jiang (2016) and Dzendzik et al.

ultimately, the correct prediction was made using ranking and the main keyword to find the correct answer, "Computer classes".


Based on the results of the proposed research, various research directions can be considered in the future.

our research goes a step further to guarantee that the learned vector indeed keep the semantic information by checking the reconstruction the symbolic semantic frame from the vector.


The retrieved sentence patterns are similar to the given text, and the vocabulary presented is also similar.

once we obtained a reliable and reasonable semantic representation in a vector form, we can devise many useful and new applications around the NLU (Figure 1).


v tag,value -the reading result of sequence of slot-tags and values -is taken from the final output of RNN at time M .

this technique is not always feasible because the scores are often in different scales, or are occasionally not provided at all (e.g., in the purely rule-based NLU systems).


Although these methods are widely used, they rely on the connectivity of the existing KB and are only suitable for predicting relationships between existing, well-connected entities (Shi and Weninger, 2018).

then, ten annotators gave subjective evaluation scores to all 500 triples.


When dealing with such massive volumes of data, it is prohibitively expensive to manually revise all the translations.

the blocks retrieved from the data stream contained 500 samples (according to González-Rubio et al.


All these works were based on SMT systems.

all techniques compute, for each sample, an uncertainty score.


The result is a large Twitter dataset, DE T F U LL , containing more than 160000 tweets.

"Target" refers to the brand concerned by the churny content and "competitor" to all other brands mentioned in the text.


where X is the targeted brand and Y any potential competitor.

we employ 256 filters with a kernel size of 2 for the convolutional layer.


Each sentence can be represented as an n×m input matrix, where n is the maximum number of words over all sentences (padding is performed to the length of the longest tweet) and m is the number of features (i.e., dimensionality of word embeddings).

this work is an intersection of (i) churn detection in social media, (ii) multilingual churn detection and (iii) churn detection in chatbots we present the related work for each domain sepa-rately.


Social media includes a wide range of platforms, however, we choose to use Twitter.

we first remove patterns such as RT, # and @ that are Twitter-specific.


Those keywords are manually selected and are assumed to be linked with or carry churny content.

an increase of 2.03% of the mean still represents an important improvement over the state-of-the-art.


As an alternative to learning a single model, we can use the text representation layer of the aforementioned single model in the word expert model.

all training examples and models in this paper, as well as the pytorch code to reproduce results is availabe 7 .


The dimensions of hidden states and fused embeddings are set to 300.

we then run two parallel GRU layers: forward GRU layer and backward GRU layer.


However, this operation is not always reasonable and sometimes the adjunct word may be far away from the target word.

the position embedding vector is obtained by looking up the randomly initialized embeddings table according to the relevant position.


Our BGAN-NMT also obtains satisfactory translation quality against other existing NMT systems.

our BGAN-NMT also obtains satisfactory translation quality against other existing NMT systems.


NER today focuses on extracting existing entities in the text.

traditional approaches label each token in texts as part of named-entity, and achieve high performance (Ratinov and Roth, 2009;Passos et al., 2014;Chiu and Nichols, 2016).


We believe LEE is a task that will significantly improve many NER and subsequent applications and improve text understanding and inference.

in many scenarios, not all of the entities are explicitly mentioned in the text.


We identify that 97.53% of the texts contain at least one latent entity and that 80.65% of the entities are latent at least 10% of the times.

when only grouping relevant entities, we then overcome this issue and scores are improved.


As shown in Figure 1b, EmbedRank++ reduces the redundancy we faced with EmbedRank.

for this user study, we randomly selected 20 documents from the Inspec and 20 documents from the DUC2001 dataset, collected 214 binary user preference votes.


Both embedding methods allow us to embed arbitrary-length sequences of words.

keyphrase extraction is a crucial component when gleaning real-time insights from large amounts of Web and social media data.


Besides the metrics above, we also compare innovation, model size and generation speed of different models.

a word "fallen leaves" is generated in the fourth line.


OpenFace (Amos et al., 2016) and FACET (iMotion, 2017) were used for visual one and Glove (Pennington et al., 2014) was used for encoding the text features.

the new memory content u (τ ) is written to the memory state according to eq.


The very recent work (Zadeh et al., 2018a) also approaches multi-view learning with recourse to a system of recurrent encoders and attention mediated by global memory fusion.

(2018b), these predictions were evaluated against the reference emotions using the criteria of mean square error (MSE) and mean absolute error (MAE), summing across 6 classes.


We would also like to thank Alona Fyshe for providing the Joint Non-Negative Sparse Embedding code.

evaluations in the next section are based on the subsets of word-pairs for which we have embedding vectors for each word.


Distributional models provide a convenient way to model semantics using dense embedding spaces derived from unsupervised learning algorithms.

as a further test of how well dimensions of embedding models correspond to human semantic knowledge, we calculated pairwise correlations, across concepts, between embedding dimensions and properties.


It achieves this by constructing real vector embeddings using bilinear logistic regression with nonzero word co-occurrences in the training corpus within a specific context.

we show that these models attain a structural composition and semantic representation that is closer to the way humans represent concepts, evaluated using human similarity judgements, human semantic feature knowledge, and neuroimaging data.


The results presented in previous section showed that ns-CRP performs the worst on Austronesian and Austro-Asiatic datasets.

the situation is slightly better in the case of Austro-Asiatic with more than 80% of the languages having meanings attested in 70% of the meanings.


In this paper, we propose a simple post-processing to tailor word embeddings in the semantics/syntax and similarity/relatedness axes without the need of additional resources.

as the results in Table 2 and Figure 3 show, the centroid method is much more sensitive to the proposed post-processing than DaM.


This could suggest other sources of semantic information that we did not incorporate in our study.

we refer to any combination of two nouns as a noun pair.


(2017b) augmented a sentiment analysis system with eye-tracking features, including first fixation durations and fixation counts.

note again that our architecture does not require the target task data to come with eye-tracking information.


More details on LSTM LMs ILP NAMAS SEQ2SEQ T3 fluency 2.22 1.30 1.51 1.40 Table 2: Average fluency ratings for each compression system in the dataset by Toutanova et al.

(2016) and Schuster and Nakajima (2012).


Namely, they found SLOR (Pauls and Klein, 2012)-sentence logprobability which is normalized by unigram logprobability and sentence length-to correlate well with acceptability judgments at the sentence level.

we are interested in the latter, which is rated on an ordinal scale from 1 (disfluent) through 3 (fluent).


move the window of γi trainable parameters discretely per segment, as illustrated in Fig.

in this case, increasing the size of the word embeddings (at least, for the frequent terms) could provide an alternative for the model to memorize parts of the data, or maybe it makes the optimization process more robust.


see ; , where several heuristics for query sentence selection have been proposed, including the entropy over the potential translations (uncertainty sampling), query by committee, and a similarity-based sentence selection method.

then we produce a final ranking by combining these rankings.


Table 4 shows the results for Google OCR.

every textline was generated with each of the 10 different settings.


The errors incurred from OCR can be quite high due to numerous factors including typefaces, paper quality, scan quality, etc.

none of the existing systems are equipped for incorrect spellings and hence these systems may be brittle (Belinkov and Bisk, 2018) when it comes to handling spelling variations in the input.


finally, we examine the impact of neural lexicon encoding when it is used for the generative ranker, and also when it is used for the entire system.


• Phase 2: we turn on the inverse parser and update all three components in one epoch.

for example, by encoding the mapping between the natural language phrase "locate in" and the freebase predicate fb:location.location.containedby, the parser can potentially link the new phrase "located at" to the same predicate.


• Class-3 function reduce completes a subtree.

since spurious logical forms are not annotated either, a direct maximum likelihood solution does not exist.


• Phase 3: finally, we allow the reconstruction loss to back-propagate to the parser, and all three components are updated as normal.

we use the semantic parser to compute the approximated posterior q(y|x).


We adopt the general encoder architecture from these works, constructing word representations from characters and using another bidirectional LSTM to encode the context vectors.

for each word in a sentence, the decoder uses a unidirectional LSTM network (figure 1 (c)) to generate a sequence of morphological category-value pairs based on the context vector h and the previous predictions.


4) We present state-of-the-art morphological tagging results on 49 languages on the UDv2.1 corpora.

to these previous works, our focus is on modeling the compositional structure of the complex morphological labels.


Object relatives, NUMBER MATCH Jules sourità l'étudiant que l' orateur <étudiant>2 endort <étudiant>1 sérieusement depuis le début.

we focus on lexical restriction, number and animacy in the definition of intervention similarity.


Table 4 shows that there is a substantial improvement in performance for both languages in the bilingual setting.

these additional views can help overcome the problems of data sparsity, and have practical implications for efficiently collecting imagetext datasets in different languages.


We show that multilingual training improves over bilingual training, and that low-resource languages benefit from training with higher-resource languages.

we train a joint model on the translation as well as comparable portions of Multi30K, and examine the performance on French and Czech.


shorter hypothesis text, the model is required to determine if the premise (i) entails, (ii) contradicts or (iii) is neutral to the hypothesis.

l=23: a home-made bomb was found on a shopping center near ambon , indonesia 's state on monday , state media said monday .


Beyond not using parallel corpora, we train our model using a single corpus.

work in sentence simplification (see Shardlow (2014) for a survey) has some similarities with sentence compression, but it differs in that the key focus is on making sentences more easily understandable rather than shorter.


Having said this, one can define word embedding models as a function: There is related work that studies the impact of these parameters (Baroni et al., 2014;Hill et al., 2014;Elekes et al., 2017).

for our purpose, only the first two values are relevant.


There are various approaches relying on n-gram corpora, such as the Google n-gram corpus.

in summary, our results indicate that one can train high-quality embedding models with ngrams if some (mild) prerequisites hold.


Experiments have shown that word embedding models are superior to conventional distributional models (Baroni et al., 2014;Mikolov et al., 2013b).

to come to general conclusions one needs to investigate general properties of the embedding models itself, i.e., examine their intrinsic suitability.


It measures distance between compound vowels using phonetic features of basic vowels which leads to inaccuracy.

a greater negative effect on the phonetic similarity between characters than any difference in tones.


稀 饭 and 泄 愤), while true nearhomonyms are encoded much further apart (e.g.

note that we use Pinyin as the phonetic representation because it is a widely accepted Romanization system (San, 2007;ISO, 2015) of Chinese syllables, used to teach pronunciation of standard Chinese.


ALINE (Kondrak, 2003) is another illustration of the challenge of manually assigning numerical values in order to accurately represent the complex relative phonetic similarity relationships across various languages.

precision and MRR: Here we evaluate the quality of the candidate ranking since in practice, downstream applications consider only a small number of possible candidates for every word.


The encodings are learned from annotated data to separately map initial and final phonemes into n-dimensional coordinates.

even DM-Rank is outperformed by the simple MED  baseline, demonstrating the inherent problem with DM's coarse encodings.


Since our primary goal was to have annotators evenly distributed over their political orientations, we did not control the distribution based on personality traits.

(2) A large source of news editorials and detailed metadata is already available (Sandhaus, 2008).


40 candidates were recruited until we had both 12 annotators with liberal ideology and 12 with conservative ideology.

we used the New York Times Annotated Corpus (Sandhaus, 2008), which comprises around 1.8 million news articles written between 1987 and 2007.


Only those examples in which all relevant terms were represented in our vector spaces were considered.

before the popularization of neural word embeddings, a number of researchers developed and evaluated methods to encode word position into distributed vector representations.


However, multiple texts are not always available for collection and the process is time-consuming.

in this case, the concrete-level matching signal is required.


Result Interpretation For Table 2 and 3 with Should-Not-Change strategies, lower is better in the first column (since a successful adversarial testing strategy will be effective at fooling the model), while higher is better in the second column (since successful adversarial training should bring the performance back up).

a proper response to the first utterance could be "Sure, I will bring you some coffee", but for the second one, the model should do anything but bring some coffee.


Evaluation Method: One of the major metrics is Completion Rate, the percentage of two bots successfully finishing the task.

a pure-model-agnostic adversary, such as works by Jia and Liang (2017) and Belinkov and Bisk (2018), does not have any access to the model outputs when creating adversarial inputs, and is thus more generalizable across models/tasks.


The Cross Convolution Network (CNN)  extends the dual encoder with a cross convolution operation.

we observed for some examples that the correct response is generic (i.e.


Predicted: url if you go down to the bottom of that tutorial i also have a post there that is a bit more detailed about my problem poster name is trent Correct: hmm!

there has been considerable effort to develop non-goal driven dialogue systems, which are able to converse with humans on an open domain (Ritter et al., 2011).


(ii) Choose which values to forget from the cell states of the child nodes, c l and c r .

we test our models on the Multi-Genre Natural Language Inference corpus (MultiNLI; williams et al., 2017).


While having a similar objective, the proposed model requires fewer model parameters than the predecessors because it does not necessarily learn each word matrix representation separately, and the number of parameters for the composition function is not proportional to the cube of the hidden state dimension.

treeLStM achieves the state-of-the-art performance among the tree-structured models in various tasks, including natural language inference and sentiment classification.


LMS differs from MV-RNN in that it does not learn a d × d matrix for each vocabulary item.

existing compositional approaches that adopt such a powerful composition function scale poorly, with parameter counts exploding as model dimension or vocabulary size grows.


For each span m that can possibly refer to an entity (i.e.

their method is limited by the dependence on a good mention spotter and by the usage of handengineered features.


The second variant (denoted by AWVw) uses the same words, but weights the vectors by PPMI score.

rather than directly using the target rankings from Section 4.1 to fine-tune the semantic space, we will generate target values for the dot products v C j • o i from these target rankings.


Our method is based on the observation that there is a tradeoff between accurately modelling similarity in a vector space, and faithfully modelling features as directions.

if o is the vector representation of a given object then we can think of the dot product v f • o as the value of object o for feature f , and in particular, we take to mean that o 2 has the feature f to a greater extent than o 1 .


Since our method does not restrict input representation, such semi-supervised methods can be easily combined.


The evaluation results are presented in the Table 3.

with any facts that relate to food but definitely not to geographical locations, sports, etc.


Interestingly enough, there is no obvious correlation between how engaging the dialogue has been perceived and simple metrics like the length of the response or the number of asked questions.

in our work, we propose to measure how much information the chit-chat bot has gathered about its human counterpart as a proxy to the engagingness of the dialogue.


In this paper, we investigate a different approach which leverages the following intuition: an engaging dialogue between two agents is a conversation that is focused and intends to discover information with the goal of increased understanding of each other.

these approaches are complementary to defining the objective for dialogue models, as an external reward can further guide the response generation and simplify learning such oneto-many mappings.


We appreciate the feedback from the reviewers.

we can consider only probabilities where F is of a particular known size.


"strengths", but also the less obvious features e.g.

we drop words based on their importance which are calculated from training data via a novel Naïve Bayes (NB) weighting technique.


When both a male and female referent are present, the model has a slight preference for generating a pronoun that matches with the subject of the sentence (which, interestingly, is the referent that is the furthest away from the pronoun).

however, these intercepts are assigned a relatively larger contribution, as their fixed position makes their contribution independent of the magnitudes of the other terms.


Our main contribution is to provide several novel ways to deconstruct supertags to create multiple alternative auxiliary tasks, which we then combine using a multi-task prediction framework and we show that this can lead to a significant improvement in supertagging accuracy.

we found in our experiments that the results were not very sensitive to the values, and the uniform distribution over all the tasks performed the best.


1 Deep neural networks have improved the accuracy of various natural language processing (NLP) tasks by performing representation learning with massive annotated datasets.

our method is language-and task-independent and is applicable to various tasks where existing multilingual models are applicable.


An intuitive approach to realize the cross-lingual transfer of a model is to utilize machine translation by either translating the training set or the model input (Wan, 2009).

we can only obtain an accurate model for a few resource-rich languages such as English.


It challenges a system to predict similarity scores between cross-lingual word pairs with sentential context provided in both languages.

given a dictionary with item pairs from source and target languages (s i , t i ), and matrices S and T that contain the vector representations corresponding to the item pairs in the columns, we follow the standard practice  to find an orthogonal alignment matrix W that minimizes the distance between the transformed matrix W S and T .


Although BERT was pretrained on a corpus comprising 3.3B words , it is reasonable to assume that it is easier to procure abundant monolingual data than parallel data.

we empirically verified that a simplistic context average baseline outperforms contextualized word embeddings in a variant of our task which relies on parallel contexts.


Using a small static dictionary as the alignment signal provides comparable results to word alignment methods relying on parallel corpora.

sentence Retrieval may lead to superficially high scores.


(2017), text-based retrieval models often handle misspellings poorly.

our contribution is to improve performance on bidirectional speech/image retrieval through better data and better models for learning fixed dimensional latent representations of both modalities.


• Speaking rate controls the speed of the synthesized audio.

learning natural language starts with grounded, contextualized speech.


Unlike sentences with coordination, sentences with different types of RCs differ from each other at a surface level (see Table 1).

we investigated whether this hierarchical organization was driven by function words that are shared among sentences sentences or whether there was evidence that LMs were tracking more abstract properties of the sentence.


Given K captions, the recall of the concept pairs in an evaluation dataset is: Recall@K is an appropriate metric because the reference captions were produced by annotators who did not need to produce any specific word when describing an image.

when using the improved decoding mechanism with re-ranking BUTR + RR, Recall@5 increases to 13.2.


It is worth noting that Kevin Lomax is Alice's son, John Milton is Kevin's father and Mary Ann Lomax is Kevin's wife.

while a good pre-trained embedding is important, our models focus on "moving" the character embeddings without affecting any other word representations.


Interestingly, on the other hand, the concept-based system is not sensitive to the indirect effect of false friends in the cross-lingual space.

several computational models of human bilingualism exist, see Li (2013) for an overview.


if they are semantically related to the features in X i ).

moreover, much of facets to avoid confusion with domains of discourse.


We considered the problem of decomposing a vector space embedding into facets, which are characterized by a set of semantically related features and a corresponding subspace of the embedding.

each word w which occurs sufficiently frequently in the document collection D = {D e | e ∈ E} is considered as a candidate feature.


6    ue-lmu-1 gives *atuengáis as the second person plural present subjunctive of atener 'to maintain'.

in agglutination, each morphological feature corresponds roughly to a single affix.


Because this lemma is a compound of Lapin 'of Lapland' and sirri 'stint', and because all vowels in the second second stem of the compound are neutral, front harmonythe default-applies.

(2019) argue that the Kirov and Cotterell model predictions align poorly with human productions, and suggest that the reported results may be uncharacteristic due to fortuitous random seeding.


We add these tags to the vocabulary and random initialize their embeddings in the embedding layer, thus the input can be described as following: where E t , E p , E li represent the token embedding matrix, position embedding matrix and linguistic feature embedding matrix, C l is the collection of all types of linguistic features and w wt , w wp , w wli are the corresponding weight scalars.

because of the differences between language model and NLU task objectives, the problem of lack of concern on some key words exists.


Besides, it is not a good way to use external features as many as possible, most likely because of the error propagation.

they proposed a feature-rich encoder to capture keywords in summarization.


The parameters in GPT are all the same for all wordpiece embeddings in the vocabulary.

we choose it as a part of our evaluation corpora because it requires the model to capture rich linguistic phenomena.


For a given target word, evaluators were presented with several timelines created by our algorithms and baseline methods (see below).

we rank the events by a combination of this prediction and the score of ByKNN.


In this work, we focus on large, significant events, since these have the most potential to affect language (Chieu and Lee, 2004).

as another example, Nelson Mandela's death is semantically the closest event to the word 'Clinton' during 2013, based on our projected embeddings.


The left side of Figure 2 displays the performance on the various experiments, revealing an interesting pattern: on the simple syntax test set, good performance is attainable regardless of the training set, while for the complex syntax test set, training on simple syntax performs inferiorly to training on complex syntax.

for both inference types, we create a variance along the syntactic complexity dimension.


7 Both corpora were crawled from the web, using ParaCrawl (Koehn et al., 2018a).

multilingual BERT model is trained on the Wikipedia pages of 104 languages with a shared subword vocabulary.


The Zipporah-based MT systems were trained similarly to other systems in the results reported here.

bERT models perform comparably to vector-mapped bWE's trained with indomain data (PSC.vecmap), and substantially better than bWE's trained on large quantities of generic, out-of-domain parallel data (WMT).


Our experiments on three diverse morphological tasks with 16 typologically diverse languages of varying degrees of data scarcity have validated that subword-level knowledge is indeed crucial for improved task performance in such lowdata setups.

the evaluation metric is per-label accuracy, i.e., a word's morphological tag is either predicted correctly or not, and there is no partial credit for the correct prediction of only a subset of features.


Overall, the segmentation based charn followed by BPE emerge as most robust choices across all languages and tasks.

for instance, Lincolnshire is assigned the type /location/county as -shire is a suffix that strongly indicates a location.


We thank the three anonymous reviewers for their helpful suggestions.

similar paterns emerge: e.g., resorting to a segmentation method based on character n-grams seems most robust across the three tasks and across languages, although there are clear outliers.


(2017) showed that this type of stack-based representation alone is sufficient for language modeling and parsing, and indeed that more involved models actually damage model performance.

this pattern of results suggests that while dependencies may be a useful tool for text analysis, but are less suited to characterizing a generation process of sentences than phrase structure grammars area.


This hypothesis is backed up by the fact that the baseline parser alone is only 3-4% UAS away from the oracle by itself, compared to about 10% away on English and Arabic.

we combine these scores using weights learned to optimize performance on the development set (Och, 2003).


A score is given to each pair consisting of a shift and its corresponding (left or right) reduction.


Once given a governor, the most recent pending word is popped out.

local scores s only depend on word indexes (i,j,k).


Finally, to further confirm the large potential of BERT, even in tasks which involve many labels, a lack of training data, and a very specific style of communication, we have obtained a clear improvement over all other systems when employing this state-of-the-art architecture, trained on manifesto quasi-sentences and further fine-tuned on motions.

2 these are unsuitable due to, in the former case, the fact that they incorporate no opinion or policy preference information, and for the latter, being unsystematic, insufficient in both quantity and coverage of the topics that appear in Hansard, and not future-proof (that is, they do not cover unseen topics that may arise (Abercrombie and Batista-Navarro, 2018b)).


7mainly considers the discrepancy of alignment and integrity between the model output and the ground-truth, it rarely inspects grammar correctness and language fluency.

then the self-attention weights W is computed as: where U a ∈ R d×d .


All the translations generated by our proposed method do not make this mistake, since it is learned and  Table 4: BLEU scores on Zh-En translation reranking task.

we need to manually construct the corpus for training D using the original parallel corpus.


Although back-translation with MAP prediction has been proved to be successful, it suffers from several apparent issues when trained with maximum likelihood estimation (MLE) only, including exposure bias and loss-evaluation mismatch.

it is worth noting that considering a baseline reward into training objective can reduce the variance of the model.


(2019) propose to pretrain cross-lingual language models for the initialization stage of unsupervised neural machine translation, which is critical to the performance of their proposed model.

n-gram matching For a translation generated by a nMT model, we need to measure the accuracy of surface word predictions.


System performance on the sliced challenge sets (Table 5) shows that performance indeed decreases with d. Results thus indicate that it is not only the presence of the phenomena that make these sets challenging, but that the challenge increases with the distance.

in this case, systems that are predisposed towards monotonic decoding are likely to present lower performance, while systems that have no predisposition as to the order of the target side tokens relative to the source-side tokens are not expected to show any change in per-formance.


For example, 62% of the dependencies in the standard UD EWT training set (Silveira et al., 2014) are between tokens that are up to one word apart.

as our extraction method is fully automatic, it allows for the compilation of much larger challenge sets over many language pairs.


Even for a high-resource language like Chinese, its vocabulary size 10k in the multilingual BERT is only half the size of that in the Chinese BERT.

the accuracy of the mapping between two regular English embeddings that are pre-trained using similar methods (e.g., CBOW or SkipGram (Mikolov et al., 2013)) could be above 95% .


Word embedding layers encode input words into continuous low-dimension vectors, followed by positional encoding layers that add position information to them.

koehn and knowles (2017) report that even RNN-based model with the attention mechanism performs worse than phrase-based statistical machine translation (koehn et al., 2007) in translating very long sentences, which challenges us to develop an NMT model that is robust to long sentences or more generally, variations in input length.


6 To avoid micro-steps of yielding peaks and thus creating noise, we smooth cosine evolution curves with a gaussian filter.

on average, they are 1.35 peaks per word for the trained model against 0.1 peak per word for our baseline condition (untrained model), showing that cosine evolution is linear in the latter but not in the former.


It is to be noted that at training time, the network was only given full captions and not isolated words.

4 We expect our model to be very efficient with the selected 80 words, as these are the main objects featured in MSCOCO.


This shows that such models perform an implicit segmentation of the spoken input in order to extract the target words.


We note from the semantics and annotated examples, we expect this sense of grow to typically be literal.

we then extracted up to 100 sentences from wikipedia that matched these syntactic patterns.


We tune the parameter I (as shown in Algorithm 1) in the range of [50,100,150].

when richer multilingual representation technique like mBERT is employed, adversarial training can still improve cross-lingual transfer performances (0.21 and 0.54 UAS over the 29 languages by using French and Russian, respectively).


here, the annotations are similar to those in the GAN setting.

for gradient reversal (GR) and GAN based adversarial objectives, we use Adam (Kingma and Ba, 2015) to optimize the discriminator parameters, and for WGAN, we use RM-SProp (Tieleman and Hinton, 2012).


2019show that the "SelfAtt-Graph" parser captures less language-specific information and performs better than the 'RNN-Stack" parser for distant target languages.

in this paper, we study cross-lingual transfer for dependency parsing.


A dependency parser consists of (1) an encoder that transforms an input text sequence into latent representations and (2) a decoding algorithm that generates the corresponding parse tree.

the parser suffers when it is transferred to a language with different language properties.


Experiments on 28 target languages demonstrate that adversarial training significantly improves the overall transfer performances under several different settings.

we leverage unlabeled sentences from auxiliary languages and adversarial training to induce language-agnostic encoders to improve the performances of the cross-lingual dependency parsing.


We compare the proposed DAH-CRF model incorporating utterance-level topic labels extracted by LDA (denoted as DAH-CRF+LDA utt ) against five strong baselines and two variants of our own models: JAS 5 : A generative joint, additive, sequential model of topics and speech acts in patient-doctor communication (Wallace et al., 2013); DRLM-Cond 6 : A latent variable recurrent neural network for DA classification (Ji et al., 2016); Bi-LSTM-CRF 7 : A hierarchical Bi-LSTM with a CRF to classify DAs (Kumar et al., 2018); CRF-ASN: An attentive structured network with a CRF for DA classification (Chen et al., 2018); SelfAtt-CRF: A hierarchical Bi-GRU with selfattention and CRF (Raheja and Tetreault, 2019); DAH-CRF+MANUAL conv : Use the manually annotated conversation-level topic labels (i.e., each utterance of the conversation shares the same 5 https://github.com/bwallace/JAS 6 https://github.com/jiyfeng/drlm 7 https://github.com/YanWenqiang/HBLSTM-CRF  topic) for DAH-CRF model training rather than the topic labels automatically acquired from LDA; DAH-CRF+LDA conv : Use conversation-level topic labels automatically acquired from LDA for DAH-CRF model training.

they applied structured attention network to the CRF layer in order to model contextual utterances and corresponding DAs together.


Examining the topic distributions in Figure 3 reveals that Directives and Commissive classes are more relevant to the topics such as food, shopping, and credit card.

our model is partially inspired by the observation that conversational utterances are normally associated with both a DA and a topic, where the former captures the social act and the latter describes the subject matter.


As can be seen from the confusion matrices shown in Figure 4, DAH-CRF+LDA utt gives improvement over SAH-CRF for all the four DA classes of DyDA.

it is not surprising that SAH-CRF makes poor prediction on the utterances of these two DAs: they share many syntactic cues, e.g., indicator words such 'okay', 'oh', and 'uh-huh', which can easily confuse the model.


A recent comparison of summaries from human experts versus extractive summarizers on forty-six topics from the TAC 2010 summarization challenge used automatic caseframe analysis, and found essentially these same properties in the human summaries, and not in the extractive ones (Cheung and Penn, 2013).

recall from section 4.3, every pair of segments in an SCU has an edge ≥ t edge ; therefore an SCU with r + 1 contributors can be decomposed into SCUs with r contributors.


(2018) all report small performance gains when incorporating audio features on top of visual features.

aSR-based Retrieval (RET) This retrieval baseline memorizes the recipe steps in the training set, and represents them each as tf-idf vectors.


The network combines information from vision and past referring expressions to resolve which object is being referred to.

note the same encoder is used to embed both r i and Q.


We generate representations f vis from an existing grounding model, and f coref from our coreference grounding model.

in realistic applications of grounding, multiple users will repeatedly refer to the same set of objects.


In each iteration, a heuristic is used to select L unlabeled examples, which are sent to annotation and added to S lab .

will assign the more subtle question "who might run?


We then compare the average ranking in each set, with English as the reference.

we conclude that grammatical gender indeed has its imprints on the representations of inanimate nouns, and that this should be taken into account when working with gendermarking languages.


Then we compare the average ranking in each of the defined sets.

zeroing the projection of a word's vector on the gender direction is not enough in order to remove all gender information from the word's representation.


However, the runtime for pool-based AL methods is increasing with the pool's size.

(2015) use the nearest neighbor in a pool of unlabeled data as a representative which is then presented to the human annotator.


where A x reshapes p θ top θ at each time step.

the model frequently violates the #PRETERM constraint, outputting an incorrect number of preterminals on about 7% of parses even after training on 100% of the data.


For many of the ETSs, no original documents (ODCs) have been provided (documents from which they have been extracted).

another possible reason for the lower performance is that our data is heterogeneous and, therefore, it is more challenging for a machine learning model to generalize.


We further refer to these sentences as fine grained evidence (FGE).

we always take the gold standard from the preceding task instead of the output of the preceding model in the pipeline.


table 1 shows the biomedical corpora we use and their entity types.


Specifically, in this example, John is labeled as PER, so PER is the only possible correct tag at that position.

a simple solution to the problem is to merge all the datasets into one giant corpus.


Kim Kardashian may be a household name now, but that wasnt always the caseand it may all be because of pal Paris.

for online settings and larger knowledge bases, an approximate search algorithm is required.


These tasks are based on conscious human judgements.

brain activity data recorded from humans processing language is arguably the most accurate mental lexical representation available (Søgaard, 2016).


(2018) review computational approaches to the study of language with neuroimaging data and show how different type of words activate neurons in different brain regions.

the available datasets are not large enough for powerful machine learning models, the recording technologies produce noisy data, and most importantly, only few datasets are publicly available.


For the sequence generation model, we implement the Long Short-Term Memory (LSTM) network with a layer of 64 hidden units while the dimension of the input event vector representation is 200.

this demonstrates the importance of the manually added event causality knowledge, and the ability of KnowSemLM to successfully capture it.


e x ⇒ e y and e x ⇒ e z becomes e x ⇒ {e y , e x }.

we group all event knowledge pairs with the same causal event, thus event e x can lead to a set of events: we store all such event causality structures in a knowledge base KB EC .


The 20NG dataset was created using the documents obtained from 20 Newsgroups and contained 11,314 training documents and 7,532 test documents.

recent studies showed that models based on neural networks can outperform conventional models (e.g., naïve Bayes) on text classification tasks (Kim, 2014;Iyyer et al., 2015;Tang et al., 2015;Dai and Le, 2015;Jin et al., 2016;Joulin et al., 2017;Shen et al., 2018).


Entity names were treated as case-insensitive.

it outperformed the conventional naïve Bayes-based model.


Relevant news articles may contain words that are indicative of a politician's stance with respect to specific issues.

information in a KB is likely to be more restricted, but more reliable, than information extracted from news articles.


The marginalization process is performed by appealing to the conjugacy between multinomial language models and the Dirichlet prior.

in the Biography dataset, we can see that using the dynamic LMP instead of the independent improves the the Beta-Bernoulli and Gamma-Poisson results by 0.01 and 0.09, respectively.


Second, MrMep performs variable-length entity pair predictor.

the results of different approaches are summarized in table 4.


As shown in Table 1, SKE has a richest vocabulary (170,206 tokens), while WebNLG has a smallest vocabulary (5,051 tokens).

neural encoder-decoder models have provided a viable new approach for jointly extracting relations and entity pairs.


Table 6 shows two examples of extracted results from MrMep (para) and MrMep (layer).

jointly extracting relation and entity pairs is far from a trivial task due that there might exist more than one relation within the text, while each target relation might correspond to more than one entity pair.


Event Detection is an important task in Information Extraction.

7) TEACHER is an adversarial imitation based knowledge distillation approach proposed by Liu et al.


On the basis of our research, we can draw a number of conclusions that are not often addressed to in publications about new neural models, their achievements and architecture.

for example in the sentence: "... finance Minister Eduardo Aninat said.


This shows that the models are better than we thought they were, and so we corrected only the test set and left the inconsistencies.

in order to progress further with this last mile, we need a better understanding of the sources of errors in NER problem; as it is stated that "The first step to address any problem is to understand it".


Instead, smart weighting under the averaging model is usually sufficient.

2) would have computationally cost of O(n A n B d) -efficient for short word lists but not when lengths n approach or exceed d. one can follow in the footsteps of Torki (2018) and represent documents as covariance-like inner products A T A, which are of dimensionality d(d + 1)/2.


Although the task is slightly different, there has been work on building 'silver standard' data from Wikipedia (Nothman et al., 2008(Nothman et al., , 2013Pan et al., 2017), using hyperlink annotations as the seed set and propagating throughout the document.

if the classifier has accurately learned to detect entities, then for all the false negatives in N , P λ (y i = O|x i ) is small, which is the goal.


One contribution of this work is the inference step (line 6), which we address using a constrained Integer Linear Program (ILP) and describe in this section.

in languages with rich morphology, certain suffixes may indicate or contraindicate a named entity.


† , + and ‡ denote baseline approaches using Discrete Only, Discrete + Continuous and Continuous Only features respectively, whereas * denotes our own approaches.

our results show that training in a multilingual setting outperforms languagespecific models for both English and Chinese.


In particular, (Yang et al., 2019) propose a method based on BERT for enhancing event trigger and argument extraction by generating more labeled data.

they are selected based on random search and performance on the validation dataset.


For a sound comparison, we use the same data split as the English baseline (as detailed in Section 3.3).

this approach relies on machine translation which suffers from error propagation.


Human annotators can easily infer this relation based on their knowledge in history, but it is difficult for machines without prior knowledge.


Case 4: (34%) Other Table 6: Error Categories and Examples in TB-Dense step.

for example, if r i,j = BEfORE, then r j,i = AfTER.


This evaluation assumes a model will work equally well for both forward and backward TB-Dense 62.9 61.9 80.4 74.7 80.5 75.7  data, which certainly holds when we explicitly impose symmetry constraints.

to summarize, our main contributions are: • We propose a deep SSVM model for event temporal relation extraction.


MD has a similar impact on entity linking performance, as it determines the upper bound of linkable mentions.

one issue is that due to the incomplete annotation, the #(e, m) counts yield p(N il|"United States") > 0, i.e.


We use a multi-class classification over the entity vocabulary, i.e.

in Table 1 we compare our results to the most recent results by Kolitsas et al.


They find that the joint objective is beneficial, such that each task improves.

the label y vector for one token v i is defined by computing the loss over the whole entity vocabulary would be infeasible, because the entity mention vocabulary is very large and the gradients for the entity classifier would exceed our GPU memory.


Here we consider both sentence-level and crosssentence linguistic indicators as indirect supervision strategies (Section 2.3).

dREAM (Sun et al., 2019): dREAM is a dataset collected from English exams for Chinese language learners.


For our current supervised upper bound (i.e., assuming we know the correct answer option, we find the silver evidence sentences from rule-based distant supervision and then feed them into the fine-tuned transformer, we get 72.8% in accuracy, which is quite close to the performance of Amazon Turkers.


It is a natural choice for human readers to use sentences from a given text to explain why they select a certain answer option in reading tests (Bax, 2013).

their experiments are primarily designed for factoid question answering, in which it is relatively easy to extract justifications since most questions are informative.


Therefore, it is hard to learn a global vector for each interlocutor from the sparse corpus.

embeddings for the speaker, addressee and observer are updated by following role-differentiated GRUs: where A t spk (A t adr / A t obv ) is the embedding for the speaker (addressee / observer) at time step t, and h t is the utterance representation obtained from the utterance encoder layer.


Researches in this area mostly focus on the dialog with two interlocutors (Maíra Gatti de Bayser et al., 2017).

similar to (He et al., 2017b;Zhou et al., 2018), and we select three metrics for MEs, which measure the following aspects.


Memory Graph Networks (MGN) (Section 2.2): Many previous work in QA or MRC systems use memory networks to evaluate multiple answer candidates with transitive reasoning, and typically store all potentially relevant raw sentences or bag-of-symbols as memory slots.

error Analysis: Table 3 shows some of the example output from the proposed model, given the input question and memory graph nodes.


While the ability of querying a personal database could lead to many potential applications, previous work in this domain (Jiang et al., 2018) is limited due to the lack of a large-scale Figure 2: Overall architecture of the Episodic Memory QA Network.

naive increase of memory slot size or retentionbased sequential update of memory slots often increase search space for answer candidates, leading to poor precision especially for the Episodic Memory QA task.


From the experiments in this part, we find that each subsection of the hierarchical representation module only leads to a slight performance drop.

the query and response matching part also leads to a significant decline.


We allow gradients to be passed through all layers of BERT, and finetune the initialized weights with the SQuAD 2.0 dataset.

for future work, we plan to generalize the relation module to other aspects of question answering, including span prediction or multi-hop reasoning.


At the CoNLL shared task 2016, Stepanov and Riccardi (2016) reported the best scores for argument extraction with F-measures of 49.64% for Arg1 and 76.51% for Arg2.

as shown in Table 7, the scores for connective identification are not as high as achieved with a specialized model (as in the baseline).


(2016) and Stepanov and Riccardi (2016), a fair comparison is that to the similar approach of Wang et al.

to the systems mentioned before, their approach involves conditional random fields (CRF) (Lafferty et al., 2001), which are generally popular for the task of sequence labeling.


While it is not as good as knowledge-intensive approaches, it clearly outperforms other models that are also trained without additional linguistic features.

for each token our model predicts one of four classes, None, Arg1, Arg2, or Conn. We illustrate the process using the first example from Table 1.


Influence Generator The first step of generation process is to infer n different topics from a related (possibly same) historical text stream and compute the topic distributions for each unique timestamp observed in the training data.

we experimented with six different sets of publication title stream data to evaluate the performance of TILM.


The input to the Influence Generator is the r×n dimensional vector of topic distributions from previous r timestamps of a related text stream, i.e., θ T −r:T −1 (assuming T as the current timestamp).

there has been a surge of research interest in the use of neural network (NN) architectures for language modeling and automatic text generation in recent years (Dieng et al., 2016;Kiros et al., 2014;Kiddon et al., 2016;Ranzato et al., 2015).


Moreover, we also showed the potential that TILM can be used as a tool to compare influences of different external streams on a particular target stream, thus facilitating crossstream influence analysis.

research on Search and relevance almost matured after 2008 within the SIGIR community [ Figure 5c], which has also been captured year # Sample Generated Title 2000 1 discovering in hierarchical rules using lexical knowledge -2 data mining criteria for tree based regression and classification 2002 3 mining frequent class sets in spatial databases 2007 -1 a framework for community identification in dynamic social networks 2009 2 learning preferences of new users in recommender systems 3 data mining for intrusion detection from outliers 2012 -1 deep model based transfer and multi task learning for biological image analysis 2015 2 a bayesian framework for estimating properties of information network 3 active learning for sparse bayesian classification Table 4: Sample titles generated by TILM for conference KDD across different year ranges effectively by TILM and apparent from the decaying trend of Figure 5d.


The exact process of generating the next word y i in the sequence is demonstrated in Figure 1.

we propose a novel language model called Topical Influence Language Model (TILM), which is a novel extension of a neural language model to capture the influences on the contents in one text stream by the evolving topics in another related (or possibly same) text stream.


the issue results from one wrong EndofUtterance prediction but affects rest of dialogue and degrades the overall quality.


D-1, D-2, D-U: The distinctiveness denotes the number of unique unigrams, bigrams, and utterances normalized by each total count (Li et al., 2016a;Xu et al., 2018).

a feed-forward network takes the current context RNN's output c t as the query and all previous context outputs from both context RNNs as keys and values to compute attention scores.


Combining Equation 7with a reward function R (BLEU, GLEU, ROUGE, DAS, QSS and ANSS), we obtain the overall loss function using the expected reward objective as follows: where R(y, y * (i) ) denotes per sentence score (reward), Y is a set of sequences sampled from the final distribution, and α and β are tunable hyperparamters.

our framework for question generation consists of a generator and an evaluator.


We also propose some novel reward functions that evaluate the syntax of the question and semantics of the question-answer pair in its entirety.

without the coverage mechanism, we see a consistent but sufficiently lower drop (1-2%) in each evaluation measure for GE ROUGE .


We set three article-related metrics to evaluate the comprehensiveness of summaries: • Jaccard Similarity Upper Bound (JS) For each summary sentence, we compute its jaccard similarity with every article sentence.

text, attention in summarization should be soft and diverse.


We follow this setting and use the feature vectors to calculate cosine similarity.

• Sentence Coverage (SC) We define those article sentences that have a jaccard similarity higher than the gold summaries JS value as covered sentence.


generate automatic translations tailored towards formality.

to address this, we work off a dataset mixed from two different domains: news and social, making it more formality-diverse (Section 5).


Given a suitable oracle, the proposed methodology can be easily extended to other psycho-linguistic preferences such as politeness.

we observe a reasonable difference in the formality scores among hypotheses with similar generation probabilities.


Large neural language models trained on massive amounts of text have emerged as a formidable strategy for Natural Language Understanding tasks.

he have no Mean noun concreteness: 4.720 Mean verb concreteness: 2.488 Fusion Model (k = 1000): GPT2-117 (k = 1000): For a brief moment , the dark green of his eye s flash the last line s of reality into existence , finally fade slowly into existence so that he could not feel it.


We finetune the pretrained model until convergence using the default hyperparameters provided in the HuggingFace repository (though we reduce batch size to fit on a single GPU), and use the finetuned model for all further evaluations.

we find that in other aspects, GPT2-117 performs no better than the Fusion Model: when k is small, the models generate text that is equally lexically under-diverse, syntactically under-complex, and repetitive -with a tendency to fall into a snowball effect of increasing overconfidence.


Empirical results show that the proposed approach consistently improves the residual-based models and exhibits desirable generalization ability.

then, based on the scaling factors, in Section 3.2, we describe how to adaptively make the best of different types of residual structure to build a learnable residual structure.


Most ML approaches to Natural Language Processing (NLP) address modern, high-resource languages (such as English or Chinese) rather than historical, low-resource languages.

table 2 shows the total amount of annotated sentences along their six NE-categories and compares this with the three major public datasets for German NER.


Subsequently, every high-resolution page (400 dpi) was digitized with ABBYY FineReader 8.0 (2005) to ABBYY-XML, which includes structural information like paragraphs, bold/italic text, images, and table blocks.

the aspect of time is important for the research on biodiversity which is constantly evolving.


Nguyen et al., 2019), which has been re- cently published during our ongoing work.

with respect to the availability of a variety of resources, there has not been much progress made until now.


In this paper, we propose to alleviate the TOI by means of overlapping sequences of data points.

we use two different values for the batch size K -the original one with K = 20, and a prime one with K = 19.


Since our method only results in P (shifted) repetitions of the dataset, each epoch using the augmented dataset would take ∼ P times longer than an epoch over the original dataset.

a first contribution in this work is a mechanism to en-sure that all token sequences are taken into account, i.e.


quences from the model distribution is directly obtained through a sequence of local sampling decisions.

we conducted a small experiment to compare the performance of cyclical training with two-stage training in terms of speed and accuracy for a fixed motif m and features ft (see [SM] Table 4, Figure 3).


The normalized distribution associated with the GAM is .

5 not only is the distilled AM much better than the initial AM, but this is an indication that P itself (for which the cross-entropy is more difficult to compute exactly) is a good approximation of the true process.


The left graph of Figure 2 illustrates such an analogical parallelogram.

finally, in Section 4, we evaluate the learnt representations to demonstrate that the found embeddings indeed respect the sought analogies, and to illustrate the benefits of analogies for the task of answer selection.


Our model can predict the next word using the embeddings of the words in the current n-gram window, in addition to the hidden state of the LSTM layer.

we found a relatively small frequency threshold θ = 5 works quite effectively.


We further analyze the prediction score stability by computing the mean standard deviation over the binned confidence intervals of the models in Figure 3a.

this could be attributed to several sources including, but not limited to, random parameter initialization, random sampling of examples during training and random dropping of neurons.


Nevertheless, a recent comparison of all submitted approaches to the cross-domain authorship attribution task at PAN 1 indicates that deep learning is currently not able to surpass traditional methods .

note that it is a common procedure to fix training and test documents in order to ensure reproducibility .


To circumvent this problem, a novel structure is proposed.

five recent available models are implemented as the classifier in ASVAET.


Recently, many attempts (Tang et al., 2016b;Pan and Wang, 2018;Liu et al., 2018;Li et al., 2018a) focus on supervised learning and pay much attention to the interaction between the aspect and the context.

from the Table 3, the ASVAET is able to improve supervised performance consistently for all classifiers.


For example, 2P-1N indicates that the sentence has two positive aspects and one negative aspect, and P-N-Nu means that the three aspects in the sentence are positive, negative, and neutral respectively.

secondly, we compare the performance of three BERT-related methods.


In such sentences, the impact of the attention distraction is not obvious and difficult to detect.

we then learn to detect the opinion snippet by self-critical reinforcement learning.


Some meaningless words around the true snippet are included, such as "are", "and" and "at".

our method determines two positions in the sentence to detect the opinion snippet towards a particular aspect, and then uses the framed content for sentiment classification.


Due to the fact that in PolEmo 2.0 there are only those annotated elements that received 2 annotations from linguists and were agreed by the super-annotator, this time we provide 8216 reviews and 57466 sentences.

using SVM with a rich set of features we obtained 90,06% (F1-score) in the task of distinguishing between genuine SNs, counterfeited SNs and nonletters.


We take the lower bound for the illustration because there are different numbers of users for each frequency, and many frequencies have no users to assign to.

the selection is done by calculating the similarity between the topics of each earlier post and the target text.


(2009) and reflects a userspecific view in contrast to the corpus labeled by others such as the SemEval 2 corpus.

(2019b), where Manhattan distance is used as the ground measurement for calculating topic similarities and the similarity threshold is set empirically at 0.8.


It can be seen that the last user in the figure (the one at the bottom line) has the greatest values for ✏ and , which means that the decay factor has a great impact on the prediction for this user than the others but the influence from the past decays comparably fastthe user is affected a lot by recent events.

after that, the outputs of the recurrent layer together with the auxiliary time differences are fed to the attention block.


The experiments are conducted using Keras 3 with TensorFlow 4 backend.

majority of the studies are conducted at the population-level which assumes that people follow a common understanding with regard to the use of language.


In linguistic studies, the notion of 'context' varies from theory to theory that some in monologism see it as 'secondary complications', whereas in dialogist theory, it considers the reflexive relation between an expression and its setting or occasion essential (Linell, 2009).

the automatic labeling may also contain a certain level of noise caused by the variation in emoticon usage and the unreliability at the user end.


Additionally, note that each sample (performance score) in the corresponding treatments of the algorithms under comparison utilize the same k th fold data, and thus the samples are generated as a function of the same input population.

the number of topics for both LDA and NMF models was determined to be 100, by computing the coherence score of several topic models obtained by varying the number of topics.


Similarly to the IRA dataset, the distribution is right-heavy.

we used these vectors as features for the fully supervised and for the distantsupervision scenarios.


Such a representation is convenient as it lies in the same space as the user representation.

we run node2vec on this graph and we extract the embeddings for the users.


While the identities of the accounts might be available, the labels to learn from would not be present.

looking at individual features, for both T1 and T2, the embeddings from U2M outperform those from U2H and from BERT.


in the latter case the user labels were only used for evaluation.


Moreover, we found that using all of the data for learning the embeddings was better than focusing only on users that we target in this study, namely left, right, and news feed, i.e., using the rest of the data adds additional context to the embedding space, and makes the target labels more contextually distinguishable.

for each two users u, v ∈ U , there is an edge in the similarity graph (u, v) ∈ E if u and v share a common hashtag or a common user mention The graph therefore uses the same information that is available to the embeddings.


The recorded information may be needed to compute the output for the current step, or it may need to be passed on to future history representations to compute a future output.

when a partial derivation reaches this position it is stopped to see if it is one of a small number of the best partial derivations which end in predicting that word.


These induced features represent the information about the derivation history which the training process has decided is relevant to estimating the output probabilities.

this model is making no a priori hard independence assumptions, just a priori soft biases.


Feedback about word overuse is helpful in terms of getting students to refine the use of vocabulary in their writing.

i know this isn't about capris, but still they go down to your knees that dosn't make since.


One instantiation of DOP which has received considerable interest is the model known as DOP1 2 (Bod 1992).

while the models of Collins (1996) and Eisner (1996) restricted the fragments to the locality of head-words, later models showed the importance of including context from higher nodes in the tree (Charniak 1997;Johnson 1998a).


Bod (2001) used an alternative technique which samples a fixed number of subtrees of each depth and which has the effect of assigning roughly equal weight to each node in the training data.

but Goodman shows that with his PCFG-reduction he can efficiently compute the aforementioned maximum constituents parse.


These sets include syntactic features (category, arguments, syntactic function, subcategorization features, etc.

for example, specific prepositions or infinitival markers are inserted.


For example, for the insertion of constituents such as auxiliaries, prepositions, and infinitive markers, the value of the target feature is the citation form of the word being inserted.

the other sentences with score 4 (54 sentences in total) differed in some way from the original but had been assigned that score by all 5 evaluators, who had judged them equivalent to the reference sentence.


This makes it very hard for word-based systems to generate the correct inflection from the English verb which does not contain sufficient information.

in Table 3 -we first swap the two words and then perform the splicing step.


In addition we are interested primarily in inducing small numbers of clusters (at most 128) from comparatively small amounts of data using limited or no sources of external knowledge, and in approaches that will work across a wide range of languages, rather than inducing large numbers (say 1000) from hundreds of millions of words.

secondly, because of the Zipfian distribution of word frequencies, simple baselines that assign each frequent word to a different class, can score rather highly, as we shall see below.


We use the symbol W to refer to the random variable related to the word, G for the associated gold standard tag, and T for the tag produced by one of our algorithms.


We can assume an additional sentence boundary token.

it is not clear that this directly measures the linguistic plausibility of the classification.


The evaluator is asked to follow the procedure depicted in Figure 2.

it was also supported in part by the Academic Frontier Project promoted by Doshisha University.


The meanings of ranks in the figure are as follows: (A) Perfect: no problem in both information and grammar; (B) Fair: easyto-understand with some unimportant information missing or flawed grammar; (C) Acceptable: broken but understandable with effort; (D) Nonsense: important information has been translated incorrectly.

for the utterance unit evaluation, we conducted several experiments while varying N from 1 to 4 for BLEU and from 1 to 5 for NIST.


The corpus contains texts of five different topic areas: schoolchildren's compositions, fiction, computer-related texts, news, and legal texts.

due to the fact that the size of the corpus reaches up to 1 million text words without punctuation characters, it may serve as a reference source for numerous future research applications.


When choosing the form of representation of the corpus it was taken into consideration that it should comply with international standards.

the development of the first version of the corpus was finished in summer of 2002.


The beginning of the work dates back to 1998 when the authors started a research project on the application of ILP (Inductive Logic Programming) learning methods for part-of-speech tagging.

the tag encoding system of the annotated Hungarian corpus was based on a technology (MSD) that has already been applied to other -mainly European -languages.


Contrary to our expectation, using only the general dictionary resulted in better performance (recall 93.0%, precision 91.0%) than with the terminology-based approach (recall 92.9%, precision 89.6%).

we find that ACK-2 can be activated by cell adhesion Cdc42-dependent manner.


Their performance is comparable to systems having participated to MUC conferences (P&R is the combined value of precision and recall).

once the system is adapted, the same strategy is adapted to the different languages.


For Asian languages, like Japanese, which makes use of 4 different writing systems (hiragana, katakana, kanji and romanji), the INTEx/UNITEx was not efficient.

for Germanic and Slavic languages, dictionaries must be modified to take into account inflectional forms.


While idioms and restricted collocations are lexical units, free combinations do not belong to the lexicon and thus cannot compose synsets in MultiWordNet.

phrasets are a useful source of information for different NLp tasks, and particularly in a multilingual environment to manage lexical gaps.


Subsequently, the dialogue acts in the plan are realized in accordance with their temporal order.

the input to the MNLG is a node (i.e., feature structure) stipulating the syntactic type of the output (e.g., sen-tence: <s), semantics and further information on the current dialogue act in PROFIt various types of information are combined within one input node.


If the local graph handed over to the MCL process is small, we might miss some of w's meanings in the corpus.

if the local graph handed over to the MCL process is small, we might miss some of w's meanings in the corpus.


For instance, damped oscillations and steps are significant for monitoring turbines.

initially BP is stable around 30 kpa until 4:23:14.


To carry out this comparison, we implemented a GR extraction mechanism for Penn Treebank This work was supported by UK EPSRC project GR/N36462/93 'Robust Accurate Statistical Parsing'.

(1998), and it is this specification called grammatical relations which we employ in our work.


7 The 'algorithms' are only evaluated on pronouns BC BU CH Cl C2  BC  ---60  0  BU  0  --70  0  CH 60  65  -75 75  Cl  -----C2 ---70 -  8 The difference between the 'worst' and the 'best' systems' mean itt performance is about 1%.

• Missing detmods, due to certain words not being assigned a determiner tag by the taggers.


It allows a dynamic tokenization into token units of varying size.

using our 2 Note that clues do not have to be correct!


Given a constraint P with segment B let V(P, B) be the set of variables of B that must take their value inside B.

the target semantics is described by the parallelism constraint X0/X1 Y0 /Y1 , which states that the tree segment X0/X1 has the same structure as the tree segment Yo/Yi.


The statistics in Table 1 show that the following measures are effective for transfer-based MT.

controlled language (Mitamura et al., 1991;Mitamura and Nyberg, 1995) is proposed for monolingual processing in order to reduce variety.


Generally speaking, a single source expression can be translated into multiple target expressions.

this result shows that the words of source sentences are translated into target words more directly in the case of the rewritten translations.


The question of the star-freeness restriction on FSIG constraints has not been studied before, possibly because of the following observations: (i) An acyclic automaton representing readings of the sentence has a central role in FSIG parsing (Tapanainen, 1997).

in order to reduce the number of contexts, i gathered unilateral contexts with the preprocessor.


It is a subset of the search graph and is computed as a byproduct of the search algorithm.

as evaluation criterion, we use the key-stroke ratio (KSR), which is the ratio of the number of key-strokes needed to produce the single reference translation using the interactive translation system divided by the number of key-strokes needed to simply type the reference translation.


This implies that the type a," is derived from the sequent one step up.

they defined rigid grammars Gn , n C N and G such that L(an ) = c(b* a*)" and L(G) = e{a, b}* For G" the number of alternations between a sequence of a's and a sequence of b's, (both of unbounded length) is bounded.


Hepple (1995) notes, each pair of decorated slashes { \" /i} can correspond to a particular grammatical phenomenon.

this strategy threatens to increase the size of the rule base as the semantics and usage of different coordinators are accommodated.


For example, we would expect the arguments in the two highly frequent intransitive 'n' and transitive `na' to provide valuable information with respect to their selectional preferences, but only those in `na' improve D2.

even by splitting the 'rip' probability over the different PP types in D2, a number of prominent PPs are left, the time indicating umA and nachD, mitD referring to the begun event, anD as date and inD as place indicator.


The NO_LEX model learns without making use of lexical information.

given an initial NER model Mo and a base labelled data set TL, the process is as follows: 1.


For instance, considering the translation "calle caner", a node for Spanish with feature "word:-2:calle", testing whether the word form at relative position -2 is "calle", will be translated into the node for Catalan "word:-2:carrer", which will test whether the -2 word is "caner".

in our opinion, the Catalan training set is not big enough and the errors in the retraining folds degrade the performance of the bootstrapped model.


We conclude with a discussion of related work, as well as limitations and planned extensions of our approach.

our work overcomes this limitation by developing a general feature space that avoids the need for individual development of features for specific classes.


The navigation module has a CORBA object to store information that it shares with the dialogue system.

these descriptions can be arbitrarily complex.


(10) Peter schlagt den Gong (mit dem KlOppel).

in the spirit of the MRS-based analysis for the German verbs gieflen and fi,illen that we have presented above, we propose that the semantic properties of the arguments of one of the most representative verbs of the removal predicates class in German, the verb wischen (wipe), which denotes a change of location, when a locatum argument is realized as its direct object (see example (8) in Section (2.2)), are captured by the following type: (15) CONTENT value of wischenioc wischen-ch-of-loc-rel ACT U (Peter) UND El (die Kreide) LiSZT El) saubern (trim; see example (9) in Section (2.2) and (16) below) is different than wischen: as (16) above captures, in German trimming necessarily results in trimming something off something else; in the case of example (9) above trimming the bush results in trimming the dry branches off the bush.


'she had him wrapped around her little finger') can be identified.

in such cases, any available clues are collected from the entry but are then weighted by testing possible forms against a corpus.


Each occurrence of a verb in these strings is linked to the morphological data in the verb's own entry, to ensure that inflected forms of a phrase (e.g.

it became apparent that there were some key areas where this approach would fall short.


However, it is hard to see how such a path could be represented as one of a finite number of classes.

a dependencies have played an important role in syntactic theory, but discovering the location of a gap corresponding to a WH phrase found in the syntactic representation of a sentence is also of interest for computational applications.


This approached was used to sentence align the Xinhua News stories.

this now gives the desired property that longer (1) translations get higher probabilities.


In dependency grammar, syntactic structure is modelled not by nested constituents but as a set of direct relations between two words.

there has also been a number of investigations on how to integrate chunk-boundary information into a parsing procedure.


Supplying the tagging constraint with the output of TnT in multi-tagging mode yields a labelled recall of 73.7%.

but as noted above, knowledge about syntactic categories is provided redundantly by the constraint grammar as well as by the tagger.


In a word level n-gram model, n must normally be set to 2 or 3 to obtain optimal performance.

2 We used the 8 most prolific authors from this collection, shown in Table 3.


Further, we assume that the decisions only depend on a limited window of fr92 = f3 _2...f3+2 around the current source word f3 and on the two predecessor concepts.

the first approach is derived from the field of statistical machine translation (Mt) and is based on the source-channel paradigm (Brown et al., 1993).


The TELDIR corpus is derived from the domain of a telephone directory assistance.

we distinguish whether a word is an initial or a non-initial word of a concept.


As the above reasoning makes clear, linearity is to some extent a matter of choice: certainly the underlying bag of words assumption, that the words are chosen independent of one another, is quite dubious.

note that the summands change sign here in the second part, and as long as the progression of terms is roughly linear, we can extend the limits in both directions without changing the overall zero value.


High segmentation accuracy is indicated by high values of both precision and recall.

the second is based on the number of words apparearing in the document's segments; this is done in the second suite and for both groups of experiments.We want to examine this influence on the length model as well as the influence of -y and r in the segmentation accuracy (as measured by B eeferman's Pk) .


In Table 4, rows 4, 5 and 6 correspond to C99, C99b and C99b,-r algorithms described in (Choi, 2000).

only if the optimal values for 7,r as well as the values of /4 a are known in advance, we can obtain the results of Table 2.


The model parameters are estimated from training examples that have been annotated with their correct class.

such cost-sensitive measures are problematic with a Naive Bayes classifier because the probabilities computed by Naive Bayes are not reliable, due to the independence assumptions it makes.


a great attention is devoted to the extraction of predicative forms.


Passive sentences also cause a problem, since passivization often changes the case of the verb.

our method consists of the following three steps: 1.


Cases where two verbs describe a continuous modification of the object in a sentence W-MV4kk4z6A Us 5.t1f4L ko (The police found the suspect, and arrested him ) In the above example, the verb `1=6A 6 (find)' and the verb `MIffil-6 (arrest)' describe a continuous modification relation.

a pair of actions (C%-l-qh%4 Vklz6A--4-6 (police finds suspect)'), (`Sc Vtti--(police arrests suspect)')) can be extracted.


The research was supported by EPSRC grant GR/M64208.

qualitative Degree VNMA: If the holding of a graded property or relationship in the source maps to the holding of a graded property or relationship in the target, then the qualitative absolute and relative degrees map over identically.


Since non-unary predicates map identically in SME as described in (Falkenhainer et al 1989), some, though not all, of the VNMAs might fall out of that identity-mapping.

such mappings are domain-independent and can be implemented by means of rules we call View Neutral Mapping Adjuncts (VNMAs).


This makes automation of this second step less critical, as long as the error detection step has a high precision (which is relevant since the repair step also needs to deal with false positives from detection).

in the future, we intend to experiment with defining variation contexts based on other, more general properties than the words themselves in order to increase recall, i.e., the number of errors detected.


Our reasoning here is that the more backchannels an individual produces, the fewer opportunities they are likely to have passed up.

although these can be substantive because they can repeat material from the partner's utterance (Clark and Schaefer, 1991), e.g., Right, okay, I'm below the fiat rocks, we will adopt (Jorafsky et al., 1998)'s terminology of continuer.


For our baseline model, we planned to insert a backchannel continuer after every n words, for some plausible value of n. This seemed to be the simplest choice in its own right.


If we now modify our test set so that it reproduces the behaviour of a speaker with a higher rate of backchannel, we see signficantly improved results.

the Map task Corpus has been segmented by hand into dialogue moves, as described in (Carletta et al., 1997).


Usually, the QA gap reveals itself at four different levels, namely, the lexical, syntactic, semantic and discourse levels.

it then uses WordNet to find other terms in the retrieved web documents that are lexically related to the expanded query terms.


In general, we need to restrict the glosses and synonyms to only those terms found in the web documents, to ensure that they are in the right context.

we successively relax the constraint to ensure precision.


For comparison we also report the performance of the Naive Bayes classifier (Duda and Hart, 1973).

for example, 1 = 2, r = 4 represents a window of two words to the left and four words to the right of the candidate noun-noun sequence.


fsq is designed to provide users a very expressive query system.

while tagging with morphosyntactic categories has become a standard for almost all corpora, more and more of them are nowadays annotated with refined syntactic information.


Future machine translation models that are sensitive to such linguistic clues might benefit even more.

when joining words, filler letters may be inserted at the joint.


The score for the unbroken compound (852) is higher than the preferred choice (825.6).

to summarize: We try to cover the entire length of the compound with known words and fillers between words.


Figure 3 depicts an example of the formulation of a query that approximates the following IE task: "which entities similar to 'receptor' interact with entities similar to 'IL-1'?".

in general, ATR in specialised domains (e.g.


The IL-5 also interacts with a series of nuclear receptors including retinoic acid receptor (RAR), thyroid hormone receptor (TR), and orphan nuclear receptors (hepatocyle nuclear receptor 4 (HNF4) and constitutive androstane receptor (CAR)] However, IL-1 does not interact with an orphan nuclear receptor known to antagonize ligand-dependent transactivation of other nuclear receptors.

since it is still not suitable for end users of such applications, we adopted another approach in which an interface GUI layer is used for the formulation of queries.


Save... -TRACT (XQuery For Atroixt) These are the result veer search ARA70 which specifically interacts with androgen receptor was also cloned recently.

xQuery,2 an xML query language, is used as an underlying query language for the GUI implemented as a part of x-TRACT.


nf="receptor") or their characteristics (e.g.

other systems that use GUI-driven query formulation, such as TAMBIS (Baker et al., 1998), usually use a predefmed ontology impose restrictions on query definition.


As well as specifying the permitted semantic atoms themselves, we also define a target model which for each atom specifies the other atoms with which it may legitimately combine.

"Data" = proportion of training data used; "Rules" = system uses rules only; "NGrams" = N-grams only; "Both" = both rules and N-grams.


The 5% error rate of the automatic tagging reduces the extraction accuracy by approximately the same amount.

the annotation of tokens rather than types is a prohibitively laborious task.


In our approach, each tag consists of a letter code for the general classification (i.e.

we distinguish three levels of ambiguity, namely: • Regular ambiguity: a word has multiple POSs which are well accepted and described in any existing lexicon, as in (4).


Sekine and Eriguchi (2000) present an interesting classification of named entity recognition systems.

it is simply, most of the time, not realistic to tag large amount of corpus (Appelt and Israel, 1999).


An important objection is that the weight of words is determined quite arbitrarily (Clark and Hockenmaier, 2002).

the resulting method (which we will call nth-tag method) yields 115 unlabelled and 639 labelled classes.


The T-CCG formalism is equivalent to the 'morphosyntactic CCG' formalism of Bozsahin (2002), where features are ordered in a join semi-lattice.

in this way, the set of expressions taking an NP obj argument is specified by means of the category description RES* ARG NP obj .


Accordingly, one can take advantage of a hierarchy of classes by combining parameter estimates of parent categories into the parameter estimates of a child category.

on top of each entity, the figure shows one of their Wikipedia categories (Film score composers and Professional wrestlers respectively), together with some of their ancestor categories in the Wikipedia taxonomy.


We have evaluated all the three approaches described in Section 3.

words which typically co-occur in certain syntactic positions with the members of that class.


(Fleischman, 2001), have similar accuracy.

for each approach we measured macro average precision, macro average recall, macro average f-measure and micro average f; for Class-Word and Class-Example micro f is equal to the overall accuracy, i.e.


For the BTEC IWSLT04 corpus, the statistics for English is given for the experiments described in Sections 3.3 and 3.5, respectively.

we obtained the output of each of the two systems under each of these translation scenarios on the CSTAR03 test corpus.


In Section 2, we will describe the computation of consensus translations with our approach.

table 4 gives examples of improved translation quality by using the consensus translation as derived from the rescored N -best lists.


Here, we propose an alignment procedure that explicitly models reordering of words in the hypotheses.

we extended the algorithm to favor more well-formed word sequences.


The trigram language model uses modified Kneser-Ney smoothing and interpolation of trigram and bigram estimates and was trained on the English side of the bitext.

bACKOFF: we are convinced that a europe of peace, not by military alliance is created.


This step is inserted before the stemming of a single word f in the backoff model described above.

we have presented a backoff model for phrasebased SMT that uses morphological abstractions to translate unseen word forms in the foreign language input.


Bmr001Note that we treat vague it as referential here even though, in the context of a coreference resolution preprocessing filter, it would make sense to treat it as nonreferential since it does not have an antecedent that it can be linked to.

each pattern is represented by a binary feature which has either the value match or nomatch.


The in-grammar results reveal an increase in WER for all the SLMs in comparison to the baseline MP3NuanceGr.

speech recognition for commercial dialogue systems has focused on grammar-based approaches despite the fact that statistical language models seem to have a better overall performance (Gorrell et al, 2002).


The database of songs and artists in this first version of the application is limited to 60 Swedish songs, 60 Swedish artists, 3 albums and 3 radio stations.

we tried to find an optimal vocabulary combining the small MP3 vocabulary of around 300 words with a smaller part of the GSLC vocabulary and the newspaper vocabulary.


Do you think there may be flights that are better options for X 1 that the system did not tell X 1 about?

when presented with dialogues generated by a system that combines user modelling with successive refinement (UMSR) and one that uses refinement without reference to a user model (SR), participants reported that the combined system provided them with a better overview of the available options and that they felt more certain to have been presented with all relevant options.


Past work on tree-structured outputs has used constraints for the k-best scoring tree (Mc-Donald et al., 2005b) or even all possible trees by using factored representations (Taskar et al., 2004;McDonald et al., 2005c).

we show in the next section that this robustness extends to approximate dependency parsing.


When combined with a discriminative online learning algorithm and a rich feature set, these models provide state-of-the-art performance across multiple languages.

in this paper we extend the MST parsing framework to incorporate higher-order feature representations of bounded-size connected subgraphs.


We use a model of sentence structure as depicted in Figure 4.

we selected a parameter k = 4 for Models 1 and 3 below, where distance is measured in terms of words, and k = 5 for Model 2 where distance is measured in terms of IGs, as a threshold value at and beyond which a dependency is considered "distant".


This measure allows for close matches in segment boundaries.

we used entropy weights (Guo et al., 2003) to weight the terms in the document matrix.


For example, the Parse Tree Path of the pair brought, ARG1 in the syntactic tree of Figure 4 is V ↑ VP ↓ NP.

ssT), the higher the accuracy is, (b) tree kernels are effective also in case of automatic parse trees and (c) as kernel combinations always improve traditional feature models, the best approach is to combine scalar-based and structured based kernels.


Measures that perform almost identically are grouped together and the MDUB values listed are averages.

we perform an extensive evaluation using artificially generated thesaurus-sense-tagged data.


For every word type in the vocabulary of the thesaurus, the index lists the categories that include it as a c-term.

even if the base WCCM is less accurate when generated from untagged text, it can still be used to provide association values suitable for most natural language applications.


This set has a retrieval Precision rP of 50% compared to either of the two original 'senses' because for any of the two senses only half of the 'retrieved' words match.

there are different types of polysemy, the most significant distinction probably being between syntactic classes of the word (e.g.


It is also possible to use graph-based clustering techniques to obtain sense representations based on sub-graph density measures (Dorow and Widdows, 2003;Bordag, 2003).

finally it is possible to provide the averages for the entire test runs comprising 1980 tests.